Dear R-users
i need your help to solve my problem in the code below, i want to simulate
two different samples R1 and R2 and each sample has 10 variables and 1000
observations so i want to simulate a data with high correlation between
var. in R1 and also in R2 and no correlation between R1 and R2 also i have
a problem with correlation coefficient between tow dichotomous var. the R-
program supports just these types of correlation coefficients such as
pearson, spearman,kendall.
thanks alot in advance
Thanoon
ords <- seq(0,1)
p <- 10
N <- 1000
percent_change <- 0.9
R1 <- as.data.frame(replicate(p, sample(ords, N, replace = T)))
R2 <- as.data.frame(replicate(p, sample(ords, N, replace = T)))
# pearson is more appropriate for dichotomous data
cor(R1, R2, method = "pearson")
# subset variable to have a stronger correlation
v1 <- R1[,1, drop = FALSE]
v1 <- R2[,1, drop = FALSE]
# randomly choose which rows to retain
keep <- sample(as.numeric(rownames(v1)), size = percent_change*nrow(v1))
change <- as.numeric(rownames(v1)[-keep])
# randomly choose new values for changing
new.change <- sample(ords, ((1-percent_change)*N)+1, replace = T)
# replace values in copy of original column
v1.samp <- v1
v1.samp[change,] <- new.change
# closer correlation
cor(v1, v1.samp, method = "pearson")
# set correlated column as one of your other columns
R1[,2] <- v1.samp
R2[,2] <- v1.samp
R1
R2
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